Propositionalization has already been shown to be a
promising approach for robustly and effectively handling relational
data sets for knowledge discovery. In this paper, we compare
up-to-date methods for propositionalization from two main groups:
logic-oriented and database-oriented techniques. Experiments using
several learning tasks — both ILP benchmarks and tasks from
recent international data mining competitions — show that both
groups have their specific advantages. While logic-oriented methods
can handle complex background knowledge and provide expressive
first-order models, database-oriented methods can be more efficient
especially on larger data sets. Obtained accuracies vary such that
a combination of the features produced by both groups seems a
further valuable venture.